fleiri gröf

library(DBI)
library(RSQLite)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)

# Tengist SQLite gagnagrunninum
conn <- dbConnect(RSQLite::SQLite(), "/Users/asdishalla/desktop/capstone-the-north-main 2/data/rotten_tomatoes.db")

# Sækir myndir með Leonardo DiCaprio úr rotten_tomatoes_movies_dicaprio_winslet töflunni
leo_movies <- dbGetQuery(conn, "
  SELECT *
  FROM rotten_tomatoes_movies_dicaprio_winslet
  WHERE actors LIKE '%Leonardo DiCaprio%'
")

dbDisconnect(conn)

# Tengist SQLite gagnagrunninum
conn <- dbConnect(RSQLite::SQLite(), "/Users/asdishalla/desktop/capstone-the-north-main 2/data/rotten_tomatoes.db")

# Sækir myndir með Kate Winslet úr rotten_tomatoes_movies_dicaprio_winslet töflunni
kate_movies <- dbGetQuery(conn, "
  SELECT *
  FROM rotten_tomatoes_movies_dicaprio_winslet
  WHERE actors LIKE '%Kate Winslet%'
")

dbDisconnect(conn)

# Connect to the SQLite database
conn <- dbConnect(SQLite(), "/Users/asdishalla/desktop/capstone-the-north-main 2/data/rotten_tomatoes.db")

# Drop the table if it exists
dbExecute(conn, "DROP TABLE IF EXISTS leo_kate_movies;")

## [1] 0

query <- "
  CREATE TABLE leo_kate_movies AS
  SELECT * 
  FROM rotten_tomatoes_movies_dicaprio_winslet
  WHERE actors LIKE '%Leonardo DiCaprio%' 
    AND actors LIKE '%Kate Winslet%'
"
# Execute the query
dbExecute(conn, query)

## [1] 0

dbDisconnect(conn)

# Load required libraries
library(dplyr)
library(ggplot2)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

# Ensure leo_movies and kate_movies are loaded and contain necessary columns, including 'Director', 'Production Company', and 'tomatometer_rating'

# Top 10 Directors for Leonardo DiCaprio (by movie count), with average Tomatometer rating
leo_top_directors <- leo_movies %>%
  filter(!is.na(directors), !is.na(tomatometer_rating)) %>%
  group_by(directors) %>%
  summarise(
    movie_count = n(),
    avg_tomatometer_rating = mean(tomatometer_rating, na.rm = TRUE)
  ) %>%
  arrange(desc(movie_count)) %>%
  slice(1:10)

# Top 10 Directors for Kate Winslet (by movie count), with average Tomatometer rating
kate_top_directors <- kate_movies %>%
  filter(!is.na(directors), !is.na(tomatometer_rating)) %>%
  group_by(directors) %>%
  summarise(
    movie_count = n(),
    avg_tomatometer_rating = mean(tomatometer_rating, na.rm = TRUE)
  ) %>%
  arrange(desc(movie_count)) %>%
  slice(1:10)

# Interactive Plot for Top Directors with Average Tomatometer Rating
leo_directors_plot <- ggplot(leo_top_directors, aes(x = reorder(directors, -avg_tomatometer_rating), y = avg_tomatometer_rating, text = paste("Movies:", movie_count))) +
  geom_bar(stat = "identity", fill = "blue") +
  labs(title = "Top 10 Directors for Leonardo DiCaprio (Average Tomatometer Rating)", x = "Director", y = "Average Tomatometer Rating") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

kate_directors_plot <- ggplot(kate_top_directors, aes(x = reorder(directors, -avg_tomatometer_rating), y = avg_tomatometer_rating, text = paste("Movies:", movie_count))) +
  geom_bar(stat = "identity", fill = "purple") +
  labs(title = "Top 10 Directors for Kate Winslet (Average Tomatometer Rating)", x = "Director", y = "Average Tomatometer Rating") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Convert ggplot to plotly for interactivity
ggplotly(leo_directors_plot, tooltip = "text")

ggplotly(kate_directors_plot, tooltip = "text")

# Top 10 Production Companies for Leonardo DiCaprio (by movie count), with average Tomatometer rating
leo_top_production <- leo_movies %>%
  filter(!is.na(`production_company`), !is.na(tomatometer_rating)) %>%
  group_by(`production_company`) %>%
  summarise(
    movie_count = n(),
    avg_tomatometer_rating = mean(tomatometer_rating, na.rm = TRUE)
  ) %>%
  arrange(desc(movie_count)) %>%
  slice(1:10)

# Top 10 Production Companies for Kate Winslet (by movie count), with average Tomatometer rating
kate_top_production <- kate_movies %>%
  filter(!is.na(`production_company`), !is.na(tomatometer_rating)) %>%
  group_by(`production_company`) %>%
  summarise(
    movie_count = n(),
    avg_tomatometer_rating = mean(tomatometer_rating, na.rm = TRUE)
  ) %>%
  arrange(desc(movie_count)) %>%
  slice(1:10)

# Interactive Plot for Top Production Companies with Average Tomatometer Rating
leo_production_plot <- ggplot(leo_top_production, aes(x = reorder(`production_company`, -avg_tomatometer_rating), y = avg_tomatometer_rating, text = paste("Movies:", movie_count))) +
  geom_bar(stat = "identity", fill = "blue") +
  labs(title = "Top 10 Production Companies for Leonardo DiCaprio (Average Tomatometer Rating)", x = "Production Company", y = "Average Tomatometer Rating") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

kate_production_plot <- ggplot(kate_top_production, aes(x = reorder(`production_company`, -avg_tomatometer_rating), y = avg_tomatometer_rating, text = paste("Movies:", movie_count))) +
  geom_bar(stat = "identity", fill = "purple") +
  labs(title = "Top 10 Production Companies for Kate Winslet (Average Tomatometer Rating)", x = "Production Company", y = "Average Tomatometer Rating") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Convert ggplot to plotly for interactivity
ggplotly(leo_production_plot, tooltip = "text")

ggplotly(kate_production_plot, tooltip = "text")